The rapid proliferation of machine l[REDACTED]g models as services has created a nascent market for model sharing, yet sustainable royalty mechanisms remain under‑explored. This article investigates how distributed model usage can be quantified, attributed, and monetized through dynamic royalty structures. We pose three research questions: (RQ1) How can usage metrics be reliably captured across...
Edge AI deployments face competing objectives of latency, energy consumption, and operational expense. This article investigates placement strategies for AI inference at the edge, balancing these dimensions through a multi-objective optimization framework. We evaluate a range of deployment options across heterogeneous edge environments and present empirical results that quantify tradeoffs betwe...
Foundation model providers are rapidly consolidating control over the most valuable AI assets — massive parameter counts, proprietary training pipelines, and exclusive access to high‑quality multimodal datasets. This convergence raises critical antitrust and governance questions: how concentrated is market power, what metrics can reliably capture that concentration, and how do these metrics evo...
Measuring latent skill portability across AI domains remains a critical challenge for series research. This article quantifies how capabilities learned in one domain can be repurposed in unrelated sectors, identifying hidden adoption bottlenecks. We introduce a metric based on latent embedding similarity and evaluate it across five benchmark transfer tasks. Our analysis reveals that transferabi...
Financial sanctions administered by state actors now target algorithmic laundering channels that dynamically reshape network topology. This paper investigates automated detection of sanction evasion through machine l[REDACTED]g, focusing on three core questions:
The rapid integration of large language models into technical documentation has sparked debate about the authenticity and longevity of AI‑generated scholarly content [1]. While early indicators suggest higher initial citation velocities for AI‑authored papers, the durability of this effect remains under‑explored [2]. This article investigates how citation frequency evolves across the first 24 m...
Scientific discovery increasingly relies on the ability to integrate heterogeneous data sources, a challenge exemplified by drug discovery and materials design where molecular, imaging, and experimental data must be synthesized to generate testable hypotheses. This article addresses the growing need for robust multimodal artificial intelligence frameworks that can accurately predict scientific ...
Scientific discovery increasingly relies on the integration of heterogeneous data modalities, including textual abstracts, experimental protocols, spectroscopic signatures, and structural diagrams. Multimodal artificial intelligence (AI) systems promise substantial gains in predictive accuracy, accelerated hypothesis generation, and reduced resource consumption across domains such as drug disco...
Artificial intelligence (AI) is rapidly transforming financial compliance, particularly in the verification of wealth register declarations and cross-border asset disclosure. Tax authorities worldwide are deploying machine l[REDACTED]g models to parse public corporate registries, property transaction logs, and offshore entity disclosures in order to detect hidden wealth and inconsistencies. Thi...
Behavioral specifications — formal, machine‑readable descriptions of desired system conduct — have emerged as a transformative mechanism for reducing reliance on costly human annotation in large‑scale AI training pipelines. Recent empirical analyses demonstrate that specification‑first fine‑tuning can achieve alignment fidelity comparable to manually curated datasets while lowering labeling exp...